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The L-band radiometer onboard the soil moisture active passive (SMAP) mission is used to retrieve sea surface salinity (SSS) over global ocean. In the Arctic seas, one of the major challenges of SSS remote sensing is the presence of sea ice. This paper proposes a data-driven ice correction (IC) algorithm which extracts emission from the water portion of measured brightness temperature (TB) in scenes mixed with water and ice. Emission of the ice portion was removed based on estimation according to the ice fraction (fice) in the satellite footprint and ice signature derived from surrounding pixels. The IC algorithm is applied to SMAP TB data to obtain TB with IC (TBIC), which are used for SSS retrieval using the standard JPL SMAP CAP processing system. We show that the algorithm is most effective near the ice edge, thereby increasing the fice threshold for possible SSS retrieval to 15% from the current 3% without IC. SMAP SSS are validated using in situ salinity collected during NASA's Ocean Melting Greenland (OMG) mission from 2016 to 2020 along the Greenland coast. The number of collocations between OMG and SMAP daily gridded salinity increased by more than 30% with IC. The statistical analysis shows a similar retrieval accuracy with or without IC, with the standard deviation of the difference between OMG and SMAP of 1.41 psu (with IC) and 1.42 psu (without IC). The bias-adjusted SMAP SSS depicts salinity patterns and gradients around Greenland consistent with OMG measurements.

Land surface temperature (LST) is a key parameter and plays an important role in hydrology, ecology, environment, and biogeochemistry. It is difficult for the existing satellites to acquire global LST with spatial and temporal consistency. The Moon-based Earth observation platform with a long life, large coverage can observe continuously the Earth, and obtain the global-scale LST. At present, various approaches for retrieving LST from passive microwave remote sensing data have been developed for the satellite remote sensing data with small and constant viewing zenith angle, however, the Moon-based Earth observation platform located outside the Earth's ionosphere has the viewing zenith angle of 0-90°. In this study, a modified physical-based method of LST retrieval from passive microwave data was developed for wide viewing zenith angles, and the LST and emissivity can be simultaneously estimated through the analysis of various atmospheric and ionosphere parameters. Three types of data, including the FengYun-3B satellite microwave radiation imager data, the multichannel Advanced Microwave Scanning Radiometer data, and Moon-based microwave radiation simulation data with the viewing zenith angles of 52-53°, 55°, and 0-90°, respectively, were used to retrieve LST. Results show that the estimation accuracy of LST decreases with the increase of viewing zenith angle. The 23.8 and 36.5 GHz brightness temperature is optimum for the LST estimation under a large-scale viewing zenith angle, and the root mean square errors of the LST are 5.18, 5.44, and 4.79 K, respectively.

Brightness temperature data from wave Imaging Radiometer using Aperture Synthesis (MIRAS) on board the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission have been used to derive the thickness of thin sea ice for the Arctic freeze-up period. To control the long-term geophysical quality for level 3 SMOS sea ice thickness products we derive a regional extent parameter that can be compared to independent standard ice extent products such as the NSIDC sea ice index. This metric allows to identify first-order quality problems such as data gaps and to observe the evolution of the Arctic sea ice growth in key regions. The regionalized SMOS sea ice thickness extent corresponds in general well with the corresponding NSIDC Sea Ice Index. The occurrence of severe RFI problems has so far mainly been limited to the initial period of the SMOS measurements during the season 2010/2011. Otherwise the comparison does not reveal any significant quality problems of the SMOS sea ice thickness data.

The NASA SMAP (Soil Moisture Active Passive) mission provides a global coverage of soil moisture measurements based on its L-band microwave radiometer every 2-3 days at about 40 km resolution. The soil moisture retrieval algorithms model the brightness temperature as a function of soil moisture, surface conditions and vegetation. External data sources inform the algorithms about the surface conditions and vegetation, which enable the retrieval of soil moisture. The inversion process contains uncertainties related to radiometer measurements, forward model assumptions and ancillary data sources. This study focuses on the uncertainties that depend on the seasonal evolution of the surface conditions and vegetation. The study compares the SMAP and core validation site (CVS) soil moisture values over a period of four years to extract the evolution of performance metrics over time. The analysis showed that most CVS that include managed agriculture exhibit significant time-dependent seasonal bias. This bias was linked to seasonal temperature cycle, which is a proxy to several features that can cause seasonally dependent errors in the SMAP product.

Surface orientation information is essential to depicting the 3-D structure of an object. This paper analyzes the linear polarization characteristics of several typical objects by simulations and measurements. Then, a polarization-based method is presented to acquire the object surface orientation information from three different linear polarization brightness temperature images by using a 94-GHz imaging radiometer. The experiments are conducted outside, where a wooden plate is placed obliquely on a metal box. Experimental results indicate that our method is capable of achieving the surface orientation information that is beneficial to recognizing the object. This method suggests possible applications for terrain models and object detections.

The Soil Moisture Active/Passive satellite microwave radiometer has been providing measurements of L-band thermal emission from Earth for more than 2 years. SMAP retrieves surface soil moisture from its brightness temperature measurements, and continues to provide science products to the user community. Even though the SMAP radiometer operates in a protected band, its measurements are still corrupted by Radio Frequency Interference (RFI) caused by illegal in-band transmissions or out-of-band emissions. The SMAP radiometer was designed to include special hardware to enable RFI detection and filtering using multiple detection algorithms. Given the good overall performance of SMAP algorithms to detect RFI sources, an automatic tool to report source properties automatically was developed and is now operational. This paper provides a preliminary analysis of the outputs of this reporting tool with a particular focus on the evolution of the RFI environment observed by SMAP during its period of operations.

Aquarius final product V5.0 has been released. The dataset includes close to four years of global radiometric measurements at L-band. The mission's objective was to monitor sea surface salinity, but other applications of its data over land and the cryosphere have been developed. For this reason, it is important to have accurate calibration over the full range of antenna temperatures from natural targets. It is also needed in order to combine Aquarius measurements with other L-band sensors. Aquarius calibration is strongly focused on the ocean. We present a research product which is part of the final release and aims at producing an accurate calibration from the low end (celestial sky) to the high end (land and ice) of the brightness temperature scale. We calibrate the Aquarius radiometers using measurements over the Sky and oceans and assess the new calibration using measurements over land.

Soil Moisture Active Passive (SMAP) mission and the Soil Moisture and Ocean Salinity (SMOS) missions provide brightness temperature and soil moisture estimates every 2–3 days. SMAP brightness temperature observations were compared with SMOS observations at 40° incidence angle. The brightness temperatures from the two missions are not consistent. SMAP observations show a warmer TB bias (about 1.27 K: V pol and 0.62 K: H pol) as compared to SMOS. SMAP and SMOS missions use different retrieval algorithms and ancillary datasets which result in further inconsistencies between their soil moisture products. The reprocessed constant-angle SMOS brightness temperatures were used in the SMAP soil moisture retrieval algorithm to develop a consistent multi-satellite product. The integrated product has an increased global revisit frequency (1 day) and period of record that is unattainable by either one of the satellites alone. Results from the development and validation of the integrated soil moisture product will be presented.

This paper describes the first results of a field experiment conducted at the Selhausen remote sensing field laboratory (Germany) over the entire growing season of a winter wheat. Brightness temperature measurements were performed with the passive microwave L-band radiometer ELBARA-II. The data were collected above two different footprints within a homogeneous winter wheat stand in order to disentangle between the radiation originating from the soil and the radiation originating from the vegetation. In a first step, the brightness temperature $(T_{\mathrm{B}})$ data collected above the first plot with the soil surface covered by a metal grid (i.e., blocking the radiations from the ground) were used to retrieve the vegetation optical depth ( $\tau$ ). In a second step, $T_{\mathrm{B}}$ data collected above the second plot without the metal grid on the soil surface were used to retrieve the soil surface moisture using several inverse modeling approaches. All modeling investigations were performed with the simple zero-order $\tau-\omega$ model. The results show that using $\tau$ data derived from the first plot as a priori information, which showed to be time, polarization, and angle dependent, allows us to improve the soil moisture retrieval, due to a better representation of the vegetation canopy effect on the measured $T_{\mathrm{B}}$ . Furthermore, the correlations between $\tau$ and different vegetation indices were analyzed and highlight the potential of $\tau$ in terms of vegetation monitoring for, e.g., all-weather measurements compared to optical measurements.

This work presents an assessment on the correlation between CyGNSS-derived Global Navigation Satellite Systems Reflectometry (GNSS-R) bistatic reflectivity Γ rl and SMAP-derived brightness temperature T B , over land surfaces. This parametric-study is performed as a function of Soil Moisture Content (SMC), and vegetation opacity τ. Several target areas are selected to evaluate potential differentiated geophysical effects on “active” (as many transmitters as navigation satellites are in view), and passive approaches. Although microwave radiometry has potentially a better sensitivity to SMC, the spatial resolution is poor ~ 40 km. On the other hand, GNSS-R bistatic coherent radar footprint is limited by half of the first Fresnel zone which provides about ~ 150 m of spatial resolution (depending on the geometry). The synergetic combination of both techniques could provide advantages with respect to active monostatic Synthetic Aperture Radar (SAR).

Spaceborne microwave radiometers with high frequency channels (150 – 183 GHz) are important for retrieving many geophysical parameters such as snowfall, ice water path, and atmospheric water vapor profiles. In order to obtain accurate retrievals from the brightness temperature measurements, the radiometers must be properly calibrated. Several methods have been developed to analyze the on-orbit calibration, including comparisons with radiative transfer models, comparisons with radiosonde profiles, and cross-calibration with similar radiometers. This paper introduces a new method to calibrate high frequency channels that utilizes TB histograms. This new method gives an independent approach that can be used by itself to analyze radiometer calibration or in conjunction with other methods to corroborate results.

Since the beginning of its routine science operation in March 2015, the NASA SMAP observatory has been returning interference-mitigated brightness temperature observations at L-band (1.41 GHz) frequency from space. The resulting data enable frequent global mapping of soil moisture with a retrieval uncertainty below 0.040 m 3 /m 3 at a 36 km spatial scale. This paper describes the development and validation of an enhanced version of the current standard soil moisture product. Compared with the standard product that is posted on a 36 km grid, the new enhanced product is posted on a 9 km grid. Derived from the same time-ordered brightness temperature observations that feed the current standard passive soil moisture product, the enhanced passive soil moisture product leverages on the Backus-Gilbert optimal interpolation technique that more fully utilizes the additional information from the original radiometer observations to achieve global mapping of soil moisture with enhanced clarity. The resulting enhanced soil moisture product was assessed using long-term in situ soil moisture observations from core validation sites located in diverse biomes and was found to exhibit an average retrieval uncertainty below 0.040 m 3 /m 3 . As of December 2016, the enhanced soil moisture product has been made available to the public from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center.

The Advanced Microwave Scanning Radiometer 2 (AMSR2) is part of the Global Change Observation Mission-Water (GCOM-W) mission. AMSR2 fills the void left by the loss of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) after almost 10 years. Both missions provide brightness temperature observations that are used to retrieve soil moisture. Merging AMSR-E and AMSR2 will help build a consistent long-term dataset. Before tackling the integration of AMSR-E and AMSR2 it is necessary to conduct a thorough validation and assessment of the AMSR2 soil moisture products. This study focuses on validation of the AMSR2 soil moisture products by comparison with in situ reference data from a set of core validation sites. Three products that rely on different algorithms were evaluated; the JAXA Soil Moisture Algorithm (JAXA), the Land Parameter Retrieval Model (LPRM), and the Single Channel Algorithm (SCA). Results indicate that overall the SCA has the best performance based upon the metrics considered.

Soil Moisture Active Passive (SMAP) mission and the ESA Soil Moisture and Ocean Salinity (SMOS) missions provide brightness temperature and soil moisture estimates every 2–3 days. SMAP brightness temperature observations were compared with SMOS observations at 40° incidence angle. The brightness temperatures from the two missions are not consistent and have a bias of about 2.7K over land with respect to each other. SMAP and SMOS missions use different retrieval algorithms and ancillary datasets which result in further inconsistencies between the soil moisture products. The reprocessed constant-angle SMOS brightness temperatures were used in the SMAP soil moisture retrieval algorithm to develop a consistent multi-satellite product. The integrated product will have an increased global revisit frequency (1 day) and period of record that would be unattainable by either one of the satellites alone. Results from the development and validation of the integrated product will be presented.

The Thermal InfraRed Sensor (TIRS), an infrared (IR) radiometer developed at Centro de Astrobiología, is one the sensors that compose the Mars Environmental Dynamics Analyzer (MEDA) onboard NASA?s Mars 2020 mission. It will measure the net thermal infrared radiation, reflected solar radiation at the surface, atmosphere temperature and surface skin brightness temperature using five different channels. The present paper provides a brief description on TIRS design and channels requirements. Then, a detailed presentation of sensor model equations and a sensitivity analysis to model uncertainties are included. Finally, accuracy and resolution calculus for each channel versus operational temperature is presented. The calculus is performed based on sensitivity equations and the estimated values for different uncertainties source

Aquarius is an L-band active/passive sensor designed to globally map sea surface salinity from space [1, 2]. Two instruments, a radar scatterometer and a radiometer, observe the same surface footprint almost simultaneously. The radiometer is the primary instrument for sensing sea surface salinity (SSS), while the scatterometer is included to provide a correction for sea surface roughness, which is a primary source of error in the salinity retrieval. Although the primary objective is the measurement of SSS, the instrument combination operates continuously, acquiring data over land and sea ice as well. An important feature of the data processing includes detection and mitigation of Radio Frequency Interference (RFI), which is done separately for both active and passive instruments. Correcting for RFI is particularly critical over ocean because of the high accuracy required in the brightness temperature measurements for SSS retrieval. It is also necessary for applications of the Aquarius data over land, where man-made interference is widespread, even though less accuracy is required in this case. This paper will provide an overview of the current status of the Aquarius RFI processing and an update on the ongoing work on the improvement of the RFI detection and mitigation performance.

Three L-band radiometers have been observing the Earth in order to retrieve soil moisture and ocean salinity. They use different instrument configurations and calibration and retrieval algorithms. In any case, the brightness temperature retrieved at the Earth surface should be consistent between all instruments. One reason for inconsistency would be the use of different approaches for the instrument calibration or the use of different models to retrieve surface brightness temperature. We report on the different approaches used for the SMOS, SMAP and Aquarius instruments and their impact on the observations consistency.

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2015), Remote Sensing: Understanding the Earth for a Safer World, 26-31 July 2015, Milan, Italy.-- 4 pages, 6 figures, 1 table.-- © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., The Dome-C region, in the East Antarctic Plateau, has been used for calibration/validation of satellite microwave radiometers since the 1970's. However, its use as an independent external target has been recently questioned due to some spatial inhomogeneities found in L-band airborne and satellite observations. This work evidences the influence of the Antarctic ice thickness spatial variations on the measured SMOS and Aquarius brightness temperatures (TB). The possible effects of subglacial water and bedrock on the acquired radiometric signals have also been analyzed. A 3-months no-daylight period during the Austral winter has been selected. Four transects over East Antarctica have been defined to study the spatial variations. A good agreement between SMOS and Aquarius TB changes and ice thickness variations over the whole Antarctica has been observed, obtaining linear correlations of 0.6-0.7 and slopes of 8.6-9.5 K/km. The subglacial lakes may affect the vertical physical temperature profile and/or the dielectric properties of the ice layers above. As expected, the subglacial bedrock is not contributing to the measured TB, since the maximum estimated L-band penetration depth is ~1-1.5 km

We discuss work at NIST aimed at developing a passive microwave brightness temperature standard. By reducing uncertainty, we can provide better calibrations for future weather and climate-monitoring radiometers. We discuss the calibration procedure used, measured data, and various theoretical and simulated results that have led to an improved understanding of the various uncertainty contributions in the measurement. We overview a Monte Carlo simulation to determine the uncertainty in target brightness temperature as a function of measurement distance and target size. We also discuss other future improvements including an improved blackbody design. The achievable calibration source brightness temperature uncertainty is expected to be reduced from the current 0.7 to 1.0 K from 18 to 65 GHz to less than 0.3 K.

Over the next several years, NASA plans to launch several earth science missions which are expected to achieve data throughputs of 5–40 terabits per day transmitted from low earth orbiting spacecraft to ground stations. The current S-band and X-band frequency allocations in use by NASA, however, are incapable of supporting the data rates required to meet this demand. As such, NASA is in the planning stages to upgrade its existing Near Earth Network (NEN) polar ground stations to support Ka-band (25.5–27 GHz) operations. Consequently, it becomes imperative that characterization of propagation effects at these NEN sites is conducted to determine expected system performance, particularly at low elevation angles (< 10deg) where spacecraft signal acquisition typically occurs. Since May 2011, NASA Glenn Research Center has installed and operated a Ka-band radiometer at the NEN site located in Svalbard, Norway. The Ka-band radiometer monitors the water vapor line, as well as 4 frequencies around 26.5 GHz at a fixed 10 deg elevation angle. Three-year data collection results indicate good agreement with models and comparable performance to previously characterized northern latitude sites in the United States, i.e., Fairbanks, Alaska. The Svalbard data is used to derive availability results for an upcoming earth-observation mission, JPSS-1, and indicate a requirement of 4 dB of atmospheric attenuation margin necessary to close the link with 99% overall system availability for the expected LEO orbital cycle, as observed from the Svalbard location.

A numerical reconstruction of the brightness temperature is examined as a potential way to improve the retrieval of salinity from Aquarius measurements closer to landwater boundaries. A test case using simulated ocean-land scenes suggest promise for the technique.

A new approach to retrieve sea surface wind speed (SWS) in tropical cyclones (TCs) from the Advanced Microwave Scanning Radiometer 2 (AMSR2) data is presented. Analysis of all six AMSR2 C- and X-band channel measurements over TCs is shown to efficiently help to separate the rain contribution. Corrected measurements at 6.9 and 10.65 GHz are then used to retrieve the SWS. Spatial and temporal collocation of AMSR2 and tropical rain measurement mission (TRMM) microwave instrument (TMI) data is then further used to empirically relate TMI rain rate (RR) product to RR estimates from AMSR2 in hurricanes. SWS estimates are validated with measurements from the stepped frequency microwave radiometer (SFMR). As further tested, more than 100 North Atlantic and North Pacific TCs are analyzed for the 2012–2014 period. Despite few particular cases, most SWS fields are in a very good agreement with TC center data on maximum wind speeds, radii of storm, and hurricane winds. As also compared, very high consistency between AMSR2 and L-band SMOS wind speed estimates are obtained, especially for the super typhoon Haiyan, to prove the high potential of AMSR2 measurements in TCs.

Aquarius is a spaceborne instrument that uses L-band radiometers to monitor sea surface salinity globally. Other applications of its data over land and the cryosphere are being developed. Combining its measurements with existing and upcoming L-band sensors will allow for long term studies. For that purpose, the radiometers calibration is critical. Aquarius measurements are currently calibrated over the oceans. They have been found too cold at the low end (celestial sky) of the brightness temperature scale, and too warm at the warm end (land and ice). We assess the impact of the antenna pattern model on the biases and propose a correction. We re-calibrate Aquarius measurements using the corrected antenna pattern and measurements over the Sky and oceans. The performances of the new calibration are evaluated using measurements over well instrument land sites.

This work presents the PAU instrument aboard the satellite INTA MicroSAT-1. The PAU instrument is a combination of a Radiometer and a GNSS Reflectometer designed to study the relationship GNSS-R observables (Delay Doppler Maps) and the L-Band brightness temperature measurements to improve the Sea Surface Salinity (SSS) retrievals. This work describes an airborne experiment conducted to preliminary test the PAU instrument GNSS-R observables sensibility to sea state and to explore other applications of the instrument

The antenna pattern and observation geometry of the SMOS zero-baseline radiometer, which is used as a reference for the SMOS brightness temperature calibration, was applied to Aquarius simulator, which is used as a reference for the Aquarius brightness temperature calibration. In the preliminary analysis, simulations carried out over a three month period show remarkable agreement between the measurements and simulations. This fundamental agreement would indicate that the brightness temperature products of the two missions should be well correlated. Some discrepancies were also found, cause of which seems to be consistent with findings of other studies and can be corrected for.

Performance of the Radio Frequency Interference (RFI) detection and mitigation algorithms used by the Aquarius microwave radiometer is demonstrated on orbit. The detection algorithm makes use of the radiometer's high over-sampling rate to identify short, pulsed increases in power that are characteristic of radar operating nearby in the microwave spectrum. The over-sampled data are downlinked to the ground, which allows the detection algorithm to be implemented in ground processing. Access to over-sampled data on the ground also enables the mitigation algorithm, which removes samples with detected RFI from subsequent averaging. The mitigation algorithm is shown to remove nearly all detected RFI. The algorithm can also be used to characterize the RFI itself - in particular the probability distribution of its strength and its geolocation. A first look at both characteristics of the RFI are also presented here. As expected, the prevalence and strength of the RFI is found to be much greater over land than ocean. Certain regions of the globe -e.g. in and around Western Europe and Eastern and Southern Asia- have stronger and more frequent RFI.

The Hurricane Imaging Radiometer (HIRAD) is a new airborne passive microwave synthetic aperture radiometer designed to provide wide swath images of ocean surface wind speed under heavy precipitation and, in particular, in tropical cyclones. It operates at 4, 5, 6 and 6.6 GHz and uses interferometric signal processing to synthesize a pushbroom imager in software from a low profile planar antenna with no mechanical scanning. The retrieval algorithm (and the HIRAD instrument itself) is a direct descendant of the nadir-only Stepped Frequency Microwave Radiometer that is used operationally by the NOAA Hurricane Research Division to monitor Tropical Cyclones [1,2]. HIRAD participated in NASA's Genesis and Rapid Intensification Processes (GRIP) mission during Fall 2010 as its first science field campaign. HIRAD produced images of upwelling brightness temperature over a ∼70 km swath width with ∼3 km spatial resolution. The calibration and image reconstruction algorithms that were used to verify HIRAD functional performance during and immediately after GRIP were only preliminary and used a number of simplifying assumptions and approximations about the instrument design and performance. The development and performance of a more detailed and complete set of algorithms are reported here.

The Microwave Radiometer (MWR) on the Aquarius/SAC-D mission measures microwave radiation from earth and intervening atmosphere in terms of brightness temperature (T b ). It takes measurements in a push-broom fashion at K- (23.8 GHz) and Ka-band (36.5 GHz) frequencies using two separate reflector antenna systems, each producing eight spot beams. Pre-launch measurements of the alignment of these beams with respect to the spacecraft coordinate system is used to geolocate the antenna foot-prints on ground. As a part of MWR's on-orbit engineering check-out, the verification of MWR's pointing accuracy is discussed here. The technique used to assess MWR's pointing involves comparing the radiometer image of land with high-resolution maps. When the beam's instantaneous field of view (IFOV) passes over a land/water boundary, the brightness temperature changes from a radiometrically “hot” land-scene to a “cold” ocean-scene. This “step-function” change in brightness temperature provides a very sensitive way to assess the mispointing error of the calculated MWR earth location (latitude/longitude) of the antenna footprints. This paper describes the algorithm used for the MWR geolocation validation assessment and preliminary results, presented for the MWR 23.8 GHz channel, show that the mispointing errors from the true coastline are close to meeting the specification.

Soil Moisture and Ocean Salinity (SMOS) brightness temperature synthesized images are obtained after a comprehensive calibration and error correction procedure. However, the final images are still contaminated by small but non-negligible spatial errors: the so-called pixel bias. These errors have been found to be very stable in the SMOS Alias-Free Field of View (AF-FoV) and can be mitigated, to a large extent, by applying a multiplicative mask to the measured brightness temperatures at the antenna plane. This paper describes the procedure to upgrade this mask to cover SMOS Extended AF­ FoV.

This paper gives an overview of the algorithm for retrieving sea surface salinity from the AQUARIUS L-band radiometer and its physical background.

The authors propose an inversion scheme devoted to the retrieval of the atmospheric specific absorption coefficient based on data collected by a ground-based microwave radiometer. The technique processes elevation scans. The vertical plan is modelled by allocating M bins in the vertical directions. The forward modelling is defined by linearization of the radiative transfer equation around a reference model of the atmospheric parameter pattern. The goal is to estimate the atmospheric attenuation along the vertical path 3 km away from the radiometer location, where a transponder calibration device for the nadir looking RA-2 altimeter on board of the ENVISAT satellite is positioned. Estimation is performed through inversion of the brightness temperature values observed at different elevation angles, provided a proper parameterization of the model space. According to the agreement of the reference model to the actual conditions two cases are given: either the solution is acceptable or it is not reliable and a different inversion scheme is to be implemented.

L-band radiometry is recognized as a technique with a significant potential for providing spatial and temporal soil moisture variations [1, 2]. As a result, satellite missions dedicated to global soil moisture monitoring have been proposed. A 2D-interferometric L-band radiometer has recently been launched onboard the European SMOS (Soil moisture and Ocean Salinity) satellite, [3], and the NASA is in preparation of a similar suite of microwave instruments as a part of the Aquarius and SMAP (Soil Moisture Active/Passive missions, [4] which have anticipated launch dates in 2011 and 2014, respectively. The reliability of soil moisture products derived from these microwave observations will depend, at least in part, on the effectiveness of accounting for vegetation and surface roughness impacts.

The recent and imminent launch of the SMOS and Aquarius satellites carrying microwave L-band radiometers provides an opportunity to map Sea Surface Salinity (SSS) globally with an expected error < 0.2 psu. However, the accuracy of retrieved SSS depends critically on brightness temperature (Tb) corrections for sea surface roughness (SSR) effects. This paper assesses the performance of representative roughness correction models when compared with published data, and applied to recently-acquired airborne L-band radiometer data. One type of model currently being used to process SMOS data combines a wind-driven gravity wave spectrum that describes SSR, with an electromagnetic (EM) model that determines microwave emissivity, to predict the Tb roughness increment relative to the flat sea response. We find that selection of both the spectral and emissivity models strongly influences the resulting (∼1 K) Tb errors. We conclude that more accurate modeling of short wavelength spectral components and their EM influence is needed, to reduce these errors to acceptable levels.

The Corbella's revision of the fundamental equation of the interferometric aperture synthesis radiometry is verified using airborne and spaceborne data. This experimental verification uses the measurements of the airborne instrument HUT-2D from the Aalto University (Helsinki), and the unique spaceborne microwave imaging radiometer by aperture synthesis (MIRAS) from the ESA's Soil Moisture and Ocean Salinity (SMOS) mission. The data acquired with those sensors support the Corbella's revision of the visibility function. The revised function predicts that visibilities depend on the contrast between the target's brightness temperature and the backward noise of the receivers emitted through the antennas.

A multilayer neural network has been developed to retrieve IPWV over land from brightness temperatures measured by the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) on board the Aqua satellite. Our study was optimized over two areas in Northern and Central Italy. Good agreements on the order of 0.24 cm and 0.33 cm rms, respectively, were found between neural network retrievals and ECMWF IPWV data for clear-sky. In the presence of clouds, an rms of the order of 0.38 cm was found for both areas.

The Radio frequency Interference Survey of Earth (RISE) is a new type of instrument used to survey and characterize the presence of Radio Frequency Interference (RFI) that can affect microwave radiometers. It consists of a combined microwave radiometer and kurtosis spectrometer with broad frequency coverage and high temporal and spectral resolution. A K-Band airborne version has been built and flown across southeast Michigan. A kurtosis detector is included in RISE to reliably detect the presence of RFI, even at very low levels, and to aid in its characterization. A radiometer is included to measure the impact of the RFI on observed brightness temperature.

Several soil moisture and sea salinity campaigns, including airborne L-band radiometer measurements, have been carried out in preparation for the ESA Soil Moisture and Ocean Salinity (SMOS) mission. The radiometer used in this context is fully polarimetric and is capable of detecting Radio Frequency Interference (RFI) using the kurtosis method. Analyses have shown that the kurtosis method generally detects RFI in an efficient manner, even though it has its shortcomings. Hence, other detection methods have been investigated as well. In particular, inspection of the 3rd and 4th Stokes parameters shows promising results possibly as a complement to the kurtosis method. The kurtosis method, however, cannot be used with SMOS data. Since SMOS is fully polarimetric, the 3rd and 4th Stokes parameter method is an option, and this has been used on a recent, fully polarimetric SMOS data set. Finally, a discussion of the variable incidence angle signature algorithm, and the possibility of using this as RFI indicator, is carried out.

In order to correct the altimeter range for tropospheric humidity, a microwave radiometer is added to altimetry missions [Envisat/microwave radiometer, Jason/Jason Microwave Radiometer, and TOPEX-Poseidon/TOPEX Microwave Radiometer (TMR)]. Over open ocean, the combination altimeter/radiometer is satisfactory. This is not the case in coastal zones, where the signal coming from the surrounding land surfaces contaminates the radiometer measurement and makes the humidity-retrieval method unsuitable. In this paper, a radiometer simulator is built, using data from a field experiment (in situ measurements and collocated TMR measurements) and analyses from a mesoscale-forecast model. This simulator is used to perform sensitivity tests and to evaluate the current methods to retrieve the wet tropospheric correction in transition areas. The purpose of this paper is to analyze and compare the performances of these methods. After examining simple correction methods (extension of the open-sea wet tropospheric correction and use of the meteorological model value), we evaluated the feasibility and performances of two methods, which propose to take into account the land-surface effect in the brightness-temperature estimation. The latter was found to give significantly better results.

Advanced Microwave Scanning Radiometer (AMSR) brightness temperature (Tb) data are typically organized into daily average images characterized by compromised temporal resolution. An alternate approach considers the location-dependent measurement time of each sample and organizes data by local time-of-day (LTOD), producing bi-daily images with high temporal locality and reduced noise. These LTOD images show greater improvement and fewer artifacts than daily images when spatial resolution enhancement algorithms are applied. LTOD ice edges are on average 8 km closer to MODIS ice edges than daily ice edges.